The medical management of rheumatoid arthritis (RA) depends on a number of medications, applied sequentially either alone or in combination. As a result, future improvement in management will come not only by use of the most effective drugs but also the moist effective sequences. This project will identify optimal therapeutic strategies for patients with RA, strategies which use the best drugs in the best order, by breaking the disease course into multiple discrete, specific treatment courses called therapeutic segments. A therapeutic segment starts with the date of onset of treatment with a drug or drug combination and ends on the date of a change in the drug treatment whether due to discontinuation, deletion of an agent or addition of an agent. A typical RA patient may have 5 to 15 therapeutic segments over the disease course. Optimal treatment will be evaluated in terms of area under the curve (AUC) disability, pain, global health, cumulative toxicity, and costs. We will complete therapeutic segment outcomes for each of the five outcomes, for each major DMARD and DMARD combination, and will identify patient and group variables which affect value of these outcomes. We will first compare alternative therapeutic strategies employing all DMARDs, without regard to sequence, for long-term outcomes using multivariate regression models. We will then evaluate the impact of RA patient characteristics on outcomes using regression analyses and classification trees and consider the effects of therapeutic segments and of the sequence of these segments. Characteristics to be evaluated include: sociodemographic variables, disease duration, prior response to treatment, prior DMARD therapy, and prior toxicity. Our next task will be to evaluate the sensitivity of the sequences and the outcomes to variation in individual patient characteristics. This work will result in statistical models for the individual outcomes (AUC disability, pain, global, health toxicity, and costs). Finally, we will construct cost- effectiveness and effectiveness/toxicity ratios for each segment and for each sequence. Results will encourage clinical decision to be made on the basis of effectiveness, cost/effectiveness, and effectiveness/toxicity which may be individualized for the particular patient. This project builds upon large, long-term, high quality data sets, and techniques for estimation of effectiveness, toxicity, and costs developed by this team, in order to provide unique and important data to guide therapeutic choices toward improved outcomes for RA patients. This research will provide knowledge of the importance and magnitude of cumulative (AUC) outcomes, the drug sequences best employed for outcome improvement, the necessity of considering individual patient variability, and will provide clinicians with better information to assist them in decision-making regarding treatment choices for their RA patients.